Expedient Prediction of Eye Opening of High-Speed Links with Input Design Space Dimensionality Reduction
Hanzhi Ma, Er‐Ping Li, Andreas C. Cangellaris, Xu Chen · 2020
We propose a new method, named Support Vector Regression-based Active Subspace, for the reduction of the dimensionality of the high-dimensional input space of design parameters pertinent to the predictive assessment of the eye opening prediction of high-speed links with IBIS-AMI transmitter and receiver equalization. We compare the method with Support Vector Regression model and Principal Component Analysis-based dimensionality reduction algorithm. Numerical results show that proposed method exhibits the best accuracy in predicting eye height, eye width, and eye width at 10-12BER in the presence of correlated design variability.